Inhibitory effects of pH, salinity, and tea polyphenols concentration on the specific spoilage organisms isolated from lightly‐salted large yellow croaker (Pseudosciaena crocea)

Abstract Proteus vulgaris and Hafnia alvei were identified as specific spoilage organisms (SSOs) isolated from the refrigerated lightly‐salted large yellow croaker (Pseudosciaena crocea). In this work, the inhibitory effects of pH, salinity, and tea polyphenols concentration on both strains were investigated. Modified Gompertz models were used to estimate the kinetic parameters μm (maximum specific growth rate) and λ (duration of lag phase) of the two strains under different conditions, demonstrating that their growth rates decreased with the decrease of pH as well as the increase of salinity and tea polyphenols concentration, and the growths of both strains stopped while the salinity and tea polyphenols concentration increased to 0.05 and 5%, respectively. Response surface methodology (RSM) based on a three‐level three‐factor Box–Behnken Design (BBD) was employed to optimize the combination of these three antibacterial factors. The results showed that the optimum inhibitory conditions were: tea polyphenols concentration 0.05%, salinity 3.46%, and pH 6.96 to inhibit the growth of P. vulgaris; tea polyphenols concentration 0.05%, salinity 3.45%, and pH 6.94 to inhibit H. alvei. Validation experiments were performed and demonstrated that under these conditions, the growth of the two SSOs could be 100% inhibited. This research provided references for the inhibition of the SSOs of lightly‐salted large yellow croaker and the extension of its shelf life.

shortens its shelf life. Microorganisms are the main factors causing the spoilage of aquatic products. One or several major microorganisms responsible for the spoilage of a given product were defined as specific spoilage organisms (SSOs) (Gram & Huss, 1996).

Previous research showed that Proteus vulgaris and Hafnia alvei
were the SSOs for the lightly-salted large yellow croaker stored at 5℃ (Guo et al., 2017). It has become a research hotspot in the field of aquatic products preservation to develop effective methods to inhibit or eliminate the SSOs and prevent spoilage (Gram & Dalgaard, 2002).
Hurdle technology can inhibit the growth of SSOs while minimizing the processing of the products, by putting microorganisms under multiple stress factors including low temperature, low water activity (Aw), acidity, or so on (Leistner & Gorris, 1995).
Hurdle technology has been applied in the preservation of many aquatic products, such as dolphinfish (Coryphaena hippurus Linnaeus) (Messina et al., 2015), hairtail (Hu et al., 2014), readyto-eat shrimp (Kanatt et al., 2006), and oyster (Chen et al., 2010).  examined the inhibitory effect of hurdle factors on the growth of Vibrio alginolyticus in lightly-salted large yellow croaker, finding salinity and pH not enough to inhibit the growth of SSOs and biological preservatives necessary. Tea polyphenols, a type of biological preservatives which are natural, antibacterial, antioxidative, and antiviral, have been widely used in food preservation. Research shows that tea polyphenols can effectively inhibit the growth of SSOs in aquatic products (Wang, 2013). Zhang et al. (2011) found that 0.2% tea polyphenols could prolong the shelf life of large yellow croaker for 7-8 days at 4℃. It is a promising preservative to inhibit the SSOs in lightly-salted yellow croaker along with salinity and pH.
Predictive microbiology effectively combines mathematical model, microbiology, and computer technology to quantitatively evaluate the growth, death and, dormancy of microorganisms. Zhou et al. (2015) used first-order and second-order kinetic models to describe the growth of Listeria monocytogenes in raw fish fillets. Vermeulen et al. (2007) developed a growth/nongrowth interface model to describe inhibitory effect of low temperature, pH, Aw, and acetic acid on the growth of Listeria spp. Response surface methodology (RSM) can simultaneously compare and optimize multiple factors and their interactions and obtain the optimal level of each factor. Jiang et al. used RSM to optimize the factors to inhibit the growth of L. monocytogenes in salmon for better preservation (Jiang et al., 2017).
The objective of this work was to investigate the inhibitory effects of pH, salinity, and tea polyphenols concentration separately on the two SSOs P. vulgaris and H. alvei isolated from lightly-salted large yellow croaker, by developing growth models to estimate and compare the kinetic parameters of the two strains under different hurdle factors stress. RSM was then used to optimize the combination of these three hurdle factors to inhibit the growth of the SSOs, which can provide a reference for extending the shelf life and improving the quality of light-salted large yellow croaker.

| Preparation of bacterial suspension samples
Lightly-salted large yellow croaker was processed by a fishery company in Ningde city, Zhejiang province, China by back-cutting, cleaning, salting, drying, and vacuum packaging. After being for 24 h until the concentration of bacterial suspension reached 10 8 CFU/ml. Finally, the suspension was diluted to 10 4 CFU/ml by gradient of sterile saline.

| Experimental design
Because of the low salinity and weak acidity of lightly-salted large yellow croaker, three factors (pH, salinity, and tea polyphenols concentration) were investigated with five levels for each factor. Based on the results of preexperiment, the salinity was set as 1, 2, 3, 4, and 5% with a pH of 7.0; pH was adjusted by HCI to 5.0, 5.5, 6.0, 6.5, and 7.0 with salinity at 0.5%; tea polyphenols concentration was set as 0.01, 0.03, 0.05, 0.07, and 0.09% with pH at 7.0 and salinity at 0.5%.
The corresponding TSB inoculation solutions were prepared accordingly and sterilized at 121℃ for 15 min.
The prepared sterile inoculation solutions were added to the sterile 96-well microtiter plate as 180 μl per well, with 20 μl 10 4 CFU/ml bacterial suspension. Four replicates were made for each condition and sterile TSB broth (salinity 0.5%, pH 7.0) was used as control. The microtiter plate was then incubated in Bioscreen, a microbial growth analyzer, at 5℃ for 10 days, and the optical density at 600 nm (OD 600 ) of the content of each well was measured and recorded every hour.

| Development of the growth kinetics models
The modified Gompertz model was used to describe the growth of two strains in different conditions and estimate the kinetic parameters (Zweitering & Jongenburger, 1990): (1) The goodness-of-fit of the models were evaluated by determinant coefficient R 2 , accuracy A f , deviation B f , and RMS, calculated as follows: where X cal is the predicted value and X obs is the measured value. The closer the R 2 , A f , and B f values are to 1, or the closer the RMS is to 0, the better the prediction is.

| Response surface methodology
Based on the results of single inhibitory factors, an RSM was used to optimize the combination of three factors with Box-Behnken Design. The experiment was designed and data were analyzed by Design-Expert 8.0.6 software with 3 levels for each factor (Table 1) and 17 different conditions were yielded. Each condition was performed in quadruplicate, and TSB broth (pH 7.3 + 0.2, NaCl 0.5%) was used as blank control group.
The inhibitory rate was used as response value to evaluate the inhibitory effect of each condition, calculated as follows (Yang et al., 2012): (2)

| Inhibitory effect of salinity concentration
Salinity is one of the most common environmental hurdle factors. Na + can separate the cytoplasm and cell wall by creating hyperosmotic environment, inhibit the synthesis of macromolecule substances, and thus  (Vyrides & Stuckey, 2009). Also, microorganisms have to consume energy to produce extracellular polymers and other substances to maintain equilibrium when the ion concentration in solution increases and will die when the pressure is too high (Blight & Ralph, 2004). The effects of salinity on growth and kinetic parameters of two bacteria strains are shown in Figures 3 and 4. The growth curves are well fitted with modified Gompertz model, as the correlation coefficients R 2 were greater than 0.9870, while Afs were between 1.000 and 1.060, Bfs were between 1.000 and 1.050, and RMS were between 0.000 and 0.070. The figures indicate that with the increase of salinity, m decreased while λ increased, demonstrating salinity has good inhibitory effect on the two bacteria. When the salinity increased to 5%, neither of the two bacteria grew. This result is consistent with other research (Olajide & Ogbeifun, 2010). The bacteria can secrete signaling molecules such as AHLs for communication to promote the growth, which is described as quorum sensing. Kong et al. (2017) found that the signaling molecules of H. alvei was most active at 2% salinity, and decreased with the increase of salinity. This can explain the inhibitory effect of salinity on the growth of the bacteria on the other hand.

| Effects of pH on the growth kinetics and model evaluation
pH can affect cell membrane permeability, biofilm formation, bacterial surface ultrastructure, and bacterial metabolism (Dai, 2016;Xiu et al., 2016). The effect of pH on the growth curve and kinetic parameters of the two strains are showed in Figures 5 and 6. The growth curves are well fitted with modified Gompertz model, as the correlation coefficients R 2 were greater than 0.980, while Afs were between 1.000 and 1.080, Bfs were between 1.000 and 1.100, and RMS were between 0.000 and 0.100. The figures indicate that with the increase of pH, m increased continuously. On the other hand, λ decreased when pH increased from 5.0 to 6.0, and kept stable when pH increased from 6.0 to 7.0.
These results coincided with the study which showed P. vulgaris could grow better in neutral and weak alkaline environment (Oladipo & Adejumobi, 2010). The result of H. alvei was similar to that of P.
vulgaris. The growth of H. alvei in different pH may be related to the formation of biofilm. Ma et al. (2017) found that the change of pH could affect the formation of biofilm of H. alvei, and the biofilm formation was the highest at pH 7.0. The pH of light-salted large yellow croaker is between 5.8 and 7.0. Within this pH range, H. alvei and P.
vulgaris can maintain growth, making them to eventually become the specific spoilage bacteria in the final products.

| Response surface model and significance analysis
On the basis of single factor experiment, response surface optimization and analysis were designed according to Box-Behnken principle, and the results were analyzed by software ( Table 2) where A is the tea polyphenols concentration, B is salinity, and C is pH. The results of coefficient significance test showed that salinity (B) had very significant (p < .01) inhibitory effect on both strains, tea polyphenols concentration (A) had significant (p < .05) inhibitory effect, while pH (C) could not effectively inhibit their growth; C 2 had significant inhibitory effect while B 2 and C 2 had no significant effect; the interaction between A and C played important role on the inhibitory effect, while other two interactions were not significant.

| Interaction analysis
To further investigate the interactions between the factors on the inhibitory effect, contour maps based on the fitting results are showed in Figures 7-9. Contour maps can directly reflect the relationships between the factors and response values, as well as the interaction effects between the factors . The steep slope of curves, elliptic contour, and dense contour lines in the graph indicated strong interactions; otherwise, the interactions were weak (Jia et al., 2010). In Figure 7, the gentle curve shows that the interaction between tea polyphenols concentration and salinity is weak. When the concentration of tea polyphenols was fixed, the inhibitory rate increased with the increase of salinity, but when the salinity was fixed, the rate barely changed with the increase of the tea polyphenols concentration. In Figure 8, the contour is elliptic, indicating that the interaction between tea polyphenols concentration and pH was strong, consistent with the regression analysis results of the previous model. Figure 9 shows that the curvature of the contour lines increases with the increase of salinity, indicating that the interaction between pH and salinity increases with the increase of salinity.

| Model optimization results and validation experiment
According to the results of RSM, the optimum antimicrobial conditions were tea polyphenols concentration 0.05%, salinity 3.46%, and pH 6.96 for P. vulgaris; tea polyphenols concentration 0.05%, salinity 3.45%, and pH 6.94 for H. alvei. Under this condition, a validation experiment was performed and result showed that the inhibitory rate was 100%, matching the predicted value, indicating that the response surface optimization results and the predicted model were reliable.

| CON CLUS IONS
Proteus vulgaris and H. alvei were specific spoilage organisms isolated from the refrigerated lightly-salted large yellow croaker. The results showed that tea polyphenols, salinity, and pH can inhibit the growth of both strains. When the concentration of tea polyphenols concentration and salinity increased to 0.05% and 5%, respectively, the strains did not grow. The results of Box-Behnken response surface showed that the optimum antibacterial parameters were as follows: tea polyphenols concentration 0.05%, salinity 3.46%, and pH 6.96 for P. vulgaris; tea polyphenols concentration 0.05%, salinity 3.45%,

E TH I C A L A PPROVA L
This study does not involve any human or animal testing.

CO N FLI C T O F I NTE R E S T
The authors declare that they do not have any conflict of interest.

I N FO R M E D CO N S E NT
Written informed consent was obtained from all study participants.

Xu Yang
https://orcid.org/0000-0003-3118-4298 F I G U R E 9 Contour map for the effects of salinity and pH on the inhibitory rate of SSOs. (a) Inhibitory rate of Proteus vulgaris under the interaction of salinity and pH; (b) Inhibitory rate of Hafnia alvei under the interaction of salinity and pH